Making the Case for Data Analytics

Last year, I wrote a blog, Did Your “Check Engine Light” Come On? that described the nearly impossible task of a single clinician to practice simply using their ability to memorize, recall and apply evidence based practice without the use of clinical and business intelligence analytics. The value of data is only understood when it is executed in a form to realize information and knowledge, with a moon shot toward wisdom.

No one would disagree with the estimates on the volume of healthcare data generated range of around 30% of the entire world's stored data. Nor is there disagreement on the potential of clinical, financial, and operational value for the healthcare industry. Because multiple CMS eCQMs programs (shown below) use data from health information systems to measure health care quality, there is a greater need for better data analytics.


Yet the data rich but information poor (DRIP) syndrome is still an issue in the performance improvement efforts of many healthcare organizations. Unlike eCQMs, which are a retrospective reporting, the value of data is realized only when the right information is presented to the right clinician at the right time to change practice. That value increases when there is more comprehensive data sharing between health care providers, health insurance partners and community stakeholders.

Many healthcare organizations are recognizing that without good data science, good delivery science, and good implementation science we will continue to struggle with the DRIP syndrome. They are also realizing the value of data downstream requires a willingness to invest in smart data analytics upstream.

Much research has been published on making the case for data analytics and becoming a data-driven organization, yet there are barriers to overcome when investing in data analytics and establishing this skill as a core competency.

  1. Lack of Internal Demand – With an health IT budget estimates as high as 10% of overall health system’s capital expenses, leadership may balk at the additional annual cost of at least $750,000 to support even a small, 3- to 5-person data science team to pull and analyze the data.
  2. Lack of qualified staff – Often health systems will promote from within interested clinical professionals rather than hire qualified data scientists. An academic medical center may leverage existing research strengths at its affiliated school or college of medicine.
  3. Ad hoc rather than system-based approach – Clinical domain experts may address smaller initiatives that do not and cannot truly span the entire enterprise.
  4. Culture – Historically clinicians have had to rely on instinct instead of data to drive decisions. Some senior leaders may not be familiar with data analytic products. These factors would not bring forth a case to put data analytics as a priority for management.
  5. Build versus buy – The question of outsourcing data analytics may make sense. Of the approximately 6,000 data scientists in the United States, only 180 are estimated to work in the hospital and health care field. Third party contractors are popping up to respond to the lack of expertise and resources available and marketing and supplying analytical services to short-handed hospitals.

All of these factors can make it difficult to embrace a move towards becoming a data-driven organization, regardless of the advantages heralded by big data.

Talented volunteers on the HIMSS C&BI committee have been applying their expertise to these problems:

Building Non-Traditional Data Sources into Meaningful Decision-Making

Data-driven healthcare decision-making is possible only when individuals have the analytic tools and appropriate view of information to determine the most effective paths to choose. This body of knowledge presents an approach to bridge the discussions between the CXO suite who are seeking answers to key strategic questions and the practical application of understanding the sources and types of data needed. | Learn More

An Operating Model, Staffing, and Skills Guidance for Analytic Maturity

Healthcare is facing unrelenting demands for cost containment, quality, and safety. Proficiency in data and analytics is the new core competency. Analytics maturity will be critical for success. This guidance provides insights to help answer to some of the most common skills and staffing resources questions your organization might face along its journey to analytics maturity. | Learn More

Population Health Management and IT Capabilities Model

The HIMSS Clinical & Business Intelligence Committee’s Population Health Task Force has created a population health management model that identifies population health domains and capabilities, and maps nearly 400 functions to Value-Based Care (VBC) payment models to help bring clarity to your population health efforts. Like a Rubik’s cube, the capabilities and payment model grid can be manipulated and can sort and filter across the domains, functions, and payment models to help you define the population health capabilities required when deploying a specific payment model. Interact with the tool and share your thoughts: We are seeking input and comments from the public for this draft version. | Learn More

Big data and analytics could transform the healthcare sector, but the industry must undergo fundamental changes before stakeholders can capture its full value.

  • Are you developing the talent needed to convert data into business value?
  • What new data sources (internal and external) are you using for healthcare analytics?
  • What population-based trends have you considered monitoring?

I encourage you to join the Clinical & Business Intelligence Community to stay up to date and learn more about the tools and educational opportunities focusing on how to mature the analytics capabilities in your organization that will help you on your journey to Turn Data to Action.

#PutData2Work | #PopHealthIT | #PrecisionHIT




About the Author:

Ellen M. Harper, DNP, RN-BC, MBA, FAAN



HIMSS Committee term: FY17-FY18

Dr. Ellen Harper, DNP, RN-BC, MBA, is President and CEO of Blue Water Informatics, a consulting company focused on using technology and informatics to support interdisciplinary, patient centered workflows. Dr Harper is also on faculty teaching advanced care delivery, performance improvement, and evidence based practice as well doing research and grant program development.

With more than 30 years of experience in health care, Ellen can leverage her professional nursing practice knowledge and health system operational experience to develop strategic initiatives and redesign of clinical business processes that result in improved quality, patient safety and reduced costs. She has made significant impact on the health care industry due to her contribution in the field of nursing informatics.

An accomplished writer with multiple published articles on evidenced based practice to achieve outcomes, staffing based on evidence, economic value of nursing, and measuring nursing value, to name a few. She lectures extensively around the world. Ellen is a graduate of American Sentinel University and holds a doctorate in nursing practice executive leadership and a MBA. She sits on numerous boards and is a fellow in the American Academy of Nurses.